Objective To establish and internally validate a predictive model for poorly differentiated adenocarcinoma based on CT imaging and tumor marker results. Methods Patients with solid and partially solid lung nodules who underwent lung nodule surgery at the Department of Thoracic Surgery, the Affiliated Brain Hospital of Nanjing Medical University in 2023 were selected and randomly divided into a training set and a validation set at a ratio of 7:3. Patients' CT features, including average density value, maximum diameter, pleural indentation sign, and bronchial inflation sign, as well as patient tumor marker results, were collected. Based on postoperative pathological results, patients were divided into a poorly differentiated adenocarcinoma group and a non-poorly differentiated adenocarcinoma group. Univariate analysis and logistic regression analysis were performed on the training set to establish the predictive model. The receiver operating characteristic (ROC) curve was used to evaluate the model's discriminability, the calibration curve to assess the model's consistency, and the decision curve to evaluate the clinical value of the model, which was then validated in the validation set. Results A total of 299 patients were included, with 103 males and 196 females, with a median age of 57.00 (51.00, 67.25) years. There were 211 patients in the training set and 88 patients in the validation set. Multivariate analysis showed that carcinoembryonic antigen (CEA) value [OR=1.476, 95%CI (1.184, 1.983), P=0.002], cytokeratin 19 fragment antigen (CYFRA21-1) value [OR=1.388, 95%CI (1.084, 1.993), P=0.035], maximum tumor diameter [OR=6.233, 95%CI (1.069, 15.415), P=0.017], and average density [OR=1.083, 95%CI (1.020, 1.194), P=0.040] were independent risk factors for solid and partially solid lung nodules as poorly differentiated adenocarcinoma. Based on this, a predictive model was constructed with an area under the ROC curve of 0.896 [95%CI (0.810, 0.982)], a maximum Youden index corresponding cut-off value of 0.103, sensitivity of 0.750, and specificity of 0.936. Using the Bootstrap method for 1000 samplings, the calibration curve predicted probability was consistent with actual risk. Decision curve analysis indicated positive benefits across all prediction probabilities, demonstrating good clinical value. Conclusion For patients with solid and partially solid lung nodules, preoperative use of CT to measure tumor average density value and maximum diameter, combined with tumor markers CEA and CYFRA21-1 values, can effectively predict whether it is poorly differentiated adenocarcinoma, allowing for early intervention.

ObjectiveThe rapid advancement of bioanalytical technologies has heightened the demand for high-throughput, label-free, and real-time cellular analysis. Electrochemical impedance spectroscopy (EIS) operating in the GHz frequency range (GHz-EIS) has emerged as a promising tool for characterizing cell suspensions due to its ability to rapidly and non-invasively capture the dielectric properties of cells and their microenvironment. Although GHz-EIS enables rapid and label-free detection of cell suspensions, significant challenges remain in interpreting GHz impedance data for complex samples, limiting the broader application of this technique in cellular research. To address these challenges, this study presents a novel method that integrates GHz-EIS with deep learning algorithms, aiming to improve the precision of cell suspension concentration identification and quantification. This method provides a more efficient and accurate solution for the analysis of GHz impedance data. MethodsThe proposed method comprises two key components: dielectric property dataset construction and backpropagation (BP) neural network modeling. Yeast cell suspensions at varying concentrations were prepared and separately introduced into a coaxial sensor for impedance measurement. The dielectric properties of these suspensions were extracted using a GHz-EIS dielectric property extraction method applied to the measured impedance data. A dielectric properties dataset incorporating concentration labels was subsequently established and divided into training and testing subsets. A BP neural network model employing specific activation functions (ReLU and Leaky ReLU) was then designed. The model was trained and tested using the constructed dataset, and optimal model parameters were obtained through this process. This BP neural network enables automated extraction and analytical processing of dielectric properties, facilitating precise recognition of cell suspension concentrations through data-driven training. ResultsThrough comparative analysis with conventional centrifugal methods, the recognized concentration values of cell suspensions showed high consistency, with relative errors consistently below 5%. Notably, high-concentration samples exhibited even smaller deviations, further validating the precision and reliability of the proposed methodology. To benchmark the recognition performance against different algorithms, two typical approaches—support vector machines (SVM) and K-nearest neighbor (KNN)—were selected for comparison. The proposed method demonstrated superior performance in quantifying cell concentrations. Specifically, the BP neural network achieved a mean absolute percentage error (MAPE) of 2.06% and an R² value of 0.997 across the entire concentration range, demonstrating both high predictive accuracy and excellent model fit. ConclusionThis study demonstrates that the proposed method enables accurate and rapid determination of unknown sample concentrations. By combining GHz-EIS with BP neural network algorithms, efficient identification of cell concentrations is achieved, laying the foundation for the development of a convenient online cell analysis platform and showing significant application prospects. Compared to typical recognition approaches, the proposed method exhibits superior capabilities in recognizing cell suspension concentrations. Furthermore, this methodology not only accelerates research in cell biology and precision medicine but also paves the way for future EIS biosensors capable of intelligent, adaptive analysis in dynamic biological research.

ObjectiveTo evaluate the clinical efficacy and safety of Huaban Jiedu Formulation （化斑解毒方， HJF） in treating psoriasis vulgaris with blood-heat syndrome. MethodsA randomized， double-blind， placebo-controlled study was conducted with 60 patients diagnosed with psoriasis vulgaris of blood-heat syndrome. Patients were randomly assigned to either a treatment group or a control group， with 30 cases in each. The treatment group received HJF granules orally， one dose a day， combined with topical Qingshi Zhiyang Ointment （青石止痒软膏）， while the control group received placebo granules， one dose a day， combined with the same topical ointment. Both groups were topically treated twice daily of 28 days treatment cours. Psoriasis area and severity index （PASI）， visual analogue scale for pruritus （VAS）， traditional Chinese medicine （TCM） syndrome scores， dermatology life quality index （DLQI）， and psoriasis life stress inventory （PLSI） were assessed before treatment and on day 14 and day 28. Response rates for PASI 50 （≥50% reduction） and PASI 75 （≥75% reduction）， as well as overall clinical efficacy， were compared between groups. Serum levels of interleukin-6 （IL-6） and interleukin-17 （IL-17） were measured before and after 28 days of treatment. Adverse reactions during treatment were recorded. ResultsAfter 28 days of treatment， both groups showed significant reductions in PASI total score， lesion area score， erythema， scaling， and infiltration scores， pruritus VAS score， TCM syndrome score， DLQI， PLSI， and serum IL-6 and IL-17 levels （P＜0.05）. Compared to the control group， the treatment group had significantly greater improvements in PASI total score and erythema score， TCM syndrome score， serum IL-6 and IL-17 levels， and PASI 50 response rate after 28 days （P＜0.05）. Between-group comparisons of score differences before and after 28-day treatment revealed that the treatment group showed significantly better improvements in PASI total， lesion area score， erythema score， TCM syndrome score， DLQI， PLSI， and inflammatory markers （P＜0.05 or P＜0.01）. The total effective rate on day 14 and day 28 was 40.00% （12/30） and 83.33% （25/30） in the treatment group， versus 6.90% （2/29） and 41.38% （12/29） in the control group， respectively. The clinical efficacy in the treatment group was significantly superior to that in the control group （P＜0.05）. Mild gastric discomfort occurred in 3 patients in the treatment group and 1 in the control group. ConclusionHJF can effectively improve skin lesions and TCM symptoms relieve pruritus， enhance quality of life， and reduce inflammatory markers IL-6 and IL-17， in patients with blood-heat syndrome of psoriasis vulgaris， with a good safety profile.

Most chemical medicines have polymorphs. The difference of medicine polymorphs in physicochemical properties directly affects the stability, efficacy, and safety of solid medicine products. Polymorphs is incomparably important to pharmaceutical chemistry, manufacturing, and control. Meantime polymorphs is a key factor for the quality of high-end drug and formulations. Polymorph prediction technology can effectively guide screening of trial experiments, and reduce the risk of missing stable crystal form in the traditional experiment. Polymorph prediction technology was firstly based on theoretical calculations such as quantum mechanics and computational chemistry, and then was developed by the key technology of machine learning using the artificial intelligence. Nowadays, the popular trend is to combine the advantages of theoretical calculation and machine learning to jointly predict crystal structure. Recently, predicting medicine polymorphs has still been a challenging problem. It is expected to learn from and integrate existing technologies to predict medicine polymorphs more accurately and efficiently.

Objective To evalucate in vitro activity of cefcapene against clinical isolated pathogenic bacteria in recent three years in China.Methods The clinical isolates were collected and the minimal inhibitory concentrations(MICs)were determined by the microbroth dilution method.Results A total of 308 pathogenic bacteria over the period 2021-2023 were studied.Cefcapene exhibited excellent antibacterial activity against Streptococci and methicillin susceptible Staphylococcus aureus(MSSA),which was similar to penicillin and cefpodoxime,and better than cefuroxime,cefaclor and cefixime.Against gram-negative bacteria,cefcapene also showed better antibacterial activity,especially against non-extended spectrum β-lactamases(ESBLs)Escherichia coli,Haemophilus influenzae and Moraxella catarrhalis,were comparable to cefixime and cefpodoxime,better than cefuroxime and cefaclor.Conclusion Cefcapene showed excellent antibacterial activity against clinical isolates from community-acquired pneumonia,include streptococcus pneumonia,MSSA,Haemophilus influenzae and Moraxella catarrhalis in recent years in China,which identical with those reported in literature ten years ago in China and foreign.It is a highly effective oral cephalosporin suitable for adults and children with respiratory tract infection,otitis media,sinusitis,urinary tract infection and other outpatient infections.

BackgroundCombination of antipsychotic drugs and modified electroconvulsive therapy （MECT） is currently a commonly used method for treating schizophrenia， but its efficacy varies among different patient groups. ObjectiveTo explore the therapeutic effects of MECT on schizophrenia patients living in different urban versus rural environments， so as to provide references for the selection of treatment plans based on patients' residence. MethodsA total of 587 patients hospitalized at Luzhou Mental Health Center， Zigong Mental Health Center and Yibin Fourth People's Hospital from May 2018 to August 2022， who met the diagnostic criteria for schizophrenia in the Diagnostic and Statistical Manual of Mental Disorders， fifth edition （DSM-5） ，were included in the study. Patients were divided into two groups： medication-only group （n=106） and MECT combined with medication group （n=481）. In MECT combined with medication group， 24 rural patients residing in urban areas were excluded， leaving the remaining patients divided into urban group （n=103） and rural group （n=354） based on their place of residence. Positive and Negative Syndrome Scale （PANSS） was used to assess the severity of symptoms. Clinical efficacy was evaluated using PANSS score reduction rate， and covariance analysis was used to compare the therapeutic effects of different patients. ResultsThe differences of reduction rate of PANSS total score， positive symptom scale score and negative symptom scale score as well as treatment effectiveness rate between MECT combined with medication group and medication-only group were statistically significant （F=11.149， 12.111， 31.725， χ²=14.010， P<0.01）. Statistically significant differences were also observed in reduction rate of PANSS total score and positive symptom subscale score as well as treatment effectiveness rate between urban and rural patients in MECT combined with medication group （F=3.946， 4.523， χ²=4.033， P<0.05）. ConclusionThe efficacy of MECT combined with medication may be superior to medication alone in the treatment of schizophrenia， and the combined therapy may be more effective in urban patients than that in rural patients， with potentially more pronounced improvements in positive symptoms.

Objective:To describe demographic,clinical and physiological characteristics,treatment between first-episode major depressive disorder(MDD)and relapse MDD,and to explore characteristics of relapse MDD.Methods:Totally 858 patients who met the diagnostic criteria for depression of the Diagnostic and Statistical Manual of Mental Disorders,Fifth Edition(DSM-5),were included by using the Mini International Neuropsychiatric Interview(MINI),Clinician-Rated Dimensions of Psychosis Symptom Severity,and Hamilton Depression Scale etc.Among them,529(58.6％)were first-episode depression and 329(36.0％)were relapsed.The differences of demographic characteristics,clinical and physiological characteristics,treatment were compared byx2test and Kruskal-Wallis rank sum test.Multivariate logistic regression was used to explore the characteristics of MDD recur-rence.Results:Compared to first-episode MDD,relapse MDD had more comorbidity(OR=2.11,95％CI:1.00-4.44),more days out of role(OR=1.26,95％CI:1.01-1.56),more history of using psychiatric drug more than one month(OR=1.41,95％CI:1.02-1.97)and electroconvulsive therapy(OR=3.23,95％CI:1.42-7.36),and higher waist-hip ratio(OR=33.88,95％CI:2.88-399.32).Conclusion:Relapse MDD has positive as-sociation with comorbidity of mental disorders,out of role,and higher waist-hip ratio.

As a supplement to randomized controlled trials， observational studies can provide evidence for the effectiveness of traditional Chinese medicine （TCM） treatment measures. They can also study influencing factors of diseases， etiology， and prognosis. However， there is a confounding effect due to the lack of randomization， which seriously affects the causal inference between the study factors and the outcome， resulting in confounding bias. Therefore， identifying and controlling confounding factors are key issues to be addressed in TCM observational studies. According to the causal network and the characteristics of TCM theory， confounding factors can be categorized into measured and unmeasured confounding factors. In addition， attention must be paid to identifying confounding factors and intermediate variables， as well as the interaction between confounding factors and study factors. For methods of controlling confounding factors， measured confounding factors can be controlled by stratification， multifactor analysis， propensity scores， and disease risk scores. Unmeasured and unknown confounding factors can be corrected using instrumental variable methods， difference-in-difference methods， and correction for underlying event rate ratios. Correcting and controlling confounding factors can ensure a balance between groups， and confounding bias can be reduced. In addition， methods such as sensitivity analysis and determination of interactions make the control of confounding factors more comprehensive. Due to the unique characteristics of TCM， observational studies of TCM face unique challenges in identifying and controlling confounding factors， including the ever-changing TCM treatment measures received by patients， the often-overlooked confounding effects in the four diagnostic information of TCM， and the lack of objective criteria for TCM evidence-based diagnosis. Some scholars have already conducted innovative explorations to address these issues， providing a methodological basis for conducting higher-quality TCM observational studies， so as to obtain more rigorous real-world evidence of TCM and gradually develop quality evaluation criteria for OS that are consistent with the characteristics of TCM.

The essential oil from Curcuma longa L. (CLEO) was extracted by steam distillation. In this study, vasorelaxant activity and mechanism of CLEO was explored. Firstly, the experimental results of isolated rat thoracic aorta ring showed that the CLEO had vasorelaxant activity. By removing the endothelium of aorta ring and pre-incubating inhibitor of endothelial nitric oxide synthase (eNOS), it was found that the vasorelaxant activity of CLEO was endothelium-dependent and related to eNOS. By human umbilical vein endothelial cells (HUVECs) model, it was found that the CLEO could promote the production of nitric oxide (NO) in HUVECs, further indicating that the vasorelaxant activity of CLEO was related to eNOS. The results of Western blot showed that CLEO could up-regulate the phosphorylation levels of phosphatidylinositol 3-kinase (PI3K), protein kinase B (Akt) and eNOS. In summary, the CLEO has vasorelaxant activity, and its mechanism is related to activating the endothelial PI3K/Akt/eNOS pathway. All animal experiments in this paper were approved by the Committee of Laboratory Animal Welfare Ethics of Chengdu University of Traditional Chinese Medicine (approval No. 2020-04).

The n-butanol fraction of Alpinia oxyphylla Fructus 70% ethanol extract was separated and purified using column chromatography with MCI Gel CHP-20, Sephadex LH-20, ODS, and silica gel, combined with semi preparative liquid phase and TLC separation methods. One new halogenated 4,5-seco-eudesmane sesquiterpenoid and two new eremophilane sesquiterpenoids were isolated and purified from the n-butanol fraction of Alpinia oxyphylla Fructus. The structures of the isolated compounds were identified using modern spectroscopic methods (1D, 2D NMR, UV, IR, MS, etc.), and the absolute configuration of the new compounds were determined using the methods of calculated ECD and induced ECD.